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[null,"1.","Record Nr.",null,"UNINA9910254076803321"]
[null,null,"Autore",null,"Zaslavski Alexander J"]
[null,null,"Titolo",null,"Numerical optimization with computational errors / / by Alexander J. Zaslavski"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-30921-8"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (ix, 304 pages)"]
[null,null,"Collana",null,"Springer Optimization and Its Applications, , 1931-6828 ; ; 108"]
[null,null,"Disciplina",null,"519.3"]
[null,null,"Soggetti",null,"Calculus of variations"]
[{"$nulls":4},"Numerical analysis"]
[{"$nulls":4},"Operations research"]
[{"$nulls":4},"Management science"]
[{"$nulls":4},"Calculus of Variations and Optimal Control; Optimization"]
[{"$nulls":4},"Numerical Analysis"]
[{"$nulls":4},"Operations Research, Management Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"1. Introduction -- 2. Subgradient Projection Algorithm -- 3. The Mirror Descent Algorithm -- 4. Gradient Algorithm with a Smooth Objective Function -- 5. An Extension of the Gradient Algorithm -- 6. Weiszfeld's Method -- 7. The Extragradient Method for Convex Optimization -- 8. A Projected Subgradient Method for Nonsmooth Problems -- 9. Proximal Point Method in Hilbert Spaces -- 10. Proximal Point Methods in Metric Spaces -- 11. Maximal Monotone Operators and the Proximal Point Algorithm -- 12. The Extragradient Method for Solving Variational Inequalities -- 13. A Common Solution of a Family of Variational Inequalities -- 14. Continuous Subgradient Method -- 15. Penalty Methods -- 16. Newton's method -- References -- Index. ."]
[null,null,null,"Sommario/riassunto","This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors are examined with the aim of determining an approximate solution. "]
[{"$nulls":4},"Researchers and students interested in the optimization theory and its applications will find this book instructive and informative. This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton’s method."]